Visual Models¶

Elliot integrates, to date, 50 recommendation models partitioned into two sets. The first set includes 38 popular models implemented in at least two of frameworks reviewed in this work (i.e., adopting a framework-wise popularity notion).

Summary¶

`ACF.ACF.ACF`(data, config, params, *args, …)	Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention
`DeepStyle.DeepStyle.DeepStyle`(data, config, …)	DeepStyle: Learning User Preferences for Visual Recommendation
`DVBPR.DVBPR.DVBPR`(data, config, params, …)	Visually-Aware Fashion Recommendation and Design with Generative Image Models
`VBPR.VBPR.VBPR`(data, config, params, *args, …)	VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback
`VNPR.visual_neural_personalized_ranking.VNPR`(…)	Visual Neural Personalized Ranking for Image Recommendation
`elliot.recommender.adversarial.AMR.AMR.AMR`(…)	Adversarial Multimedia Recommender

ACF¶

class elliot.recommender.visual_recommenders.ACF.ACF.ACF(data, config, params, *args, **kwargs)[source]¶

Bases: elliot.recommender.recommender_utils_mixin.RecMixin, elliot.recommender.base_recommender_model.BaseRecommenderModel

Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention

For further details, please refer to the paper

Parameters

lr – Learning rate
epochs – Number of epochs
factors – Number of latent factors
batch_size – Batch size
l_w – Regularization coefficient
layers_component – Tuple with number of units for each attentive layer (component-level)
layers_item – Tuple with number of units for each attentive layer (item-level)

To include the recommendation model, add it to the config file adopting the following pattern:

models:
  ACF:
    meta:
      save_recs: True
    lr: 0.0005
    epochs: 50
    factors: 100
    batch_size: 128
    l_w: 0.000025
    layers_component: (64, 1)
    layers_item: (64, 1)

DeepStyle¶

class elliot.recommender.visual_recommenders.DeepStyle.DeepStyle.DeepStyle(data, config, params, *args, **kwargs)[source]¶

Bases: elliot.recommender.recommender_utils_mixin.RecMixin, elliot.recommender.base_recommender_model.BaseRecommenderModel

DeepStyle: Learning User Preferences for Visual Recommendation

For further details, please refer to the paper

Parameters

lr – Learning rate
epochs – Number of epochs
factors – Number of latent factors
batch_size – Batch size
l_w – Regularization coefficient

To include the recommendation model, add it to the config file adopting the following pattern:

models:
  DeepStyle:
    meta:
      save_recs: True
    lr: 0.0005
    epochs: 50
    factors: 100
    batch_size: 128
    l_w: 0.000025

DVBPR¶

class elliot.recommender.visual_recommenders.DVBPR.DVBPR.DVBPR(data, config, params, *args, **kwargs)[source]¶

Bases: elliot.recommender.recommender_utils_mixin.RecMixin, elliot.recommender.base_recommender_model.BaseRecommenderModel

Visually-Aware Fashion Recommendation and Design with Generative Image Models

For further details, please refer to the paper

Parameters

lr – Learning rate
epochs – Number of epochs
factors – Number of latent factors
batch_size – Batch size
lambda_1 – Regularization coefficient
lambda_2 – CNN regularization coefficient

To include the recommendation model, add it to the config file adopting the following pattern:

models:
  DVBPR:
    meta:
      save_recs: True
    lr: 0.0001
    epochs: 50
    factors: 100
    batch_size: 128
    lambda_1: 0.0001
    lambda_2: 1.0

VBPR¶

class elliot.recommender.visual_recommenders.VBPR.VBPR.VBPR(data, config, params, *args, **kwargs)[source]¶

Bases: elliot.recommender.recommender_utils_mixin.RecMixin, elliot.recommender.base_recommender_model.BaseRecommenderModel

VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback

For further details, please refer to the paper

Parameters

lr – Learning rate
epochs – Number of epochs
factors – Number of latent factors
factors_d – Dimension of visual factors
batch_size – Batch size
l_w – Regularization coefficient
l_b – Regularization coefficient of bias
l_e – Regularization coefficient of projection matrix

To include the recommendation model, add it to the config file adopting the following pattern:

models:
  VBPR:
    meta:
      save_recs: True
    lr: 0.0005
    epochs: 50
    factors: 100
    factors_d: 20
    batch_size: 128
    l_w: 0.000025
    l_b: 0
    l_e: 0.002

VNPR¶

class elliot.recommender.visual_recommenders.VNPR.visual_neural_personalized_ranking.VNPR(data, config, params, *args, **kwargs)[source]¶

Bases: elliot.recommender.recommender_utils_mixin.RecMixin, elliot.recommender.base_recommender_model.BaseRecommenderModel

Visual Neural Personalized Ranking for Image Recommendation

For further details, please refer to the paper

Parameters

lr – Learning rate
epochs – Number of epochs
mf_factors: – Number of latent factors for Matrix Factorization:
mlp_hidden_size – Tuple with number of units for each multi-layer perceptron layer
prob_keep_dropout – Dropout rate for multi-layer perceptron
batch_size – Batch size
l_w – Regularization coefficient

To include the recommendation model, add it to the config file adopting the following pattern:

models:
  VNPR:
    meta:
      save_recs: True
    lr: 0.001
    epochs: 50
    mf_factors: 10
    mlp_hidden_size: (32, 1)
    prob_keep_dropout: 0.2
    batch_size: 64
    l_w: 0.001

AMR¶

class elliot.recommender.adversarial.AMR.AMR(data, config, params, *args, **kwargs)[source]¶

Bases: elliot.recommender.recommender_utils_mixin.RecMixin, elliot.recommender.base_recommender_model.BaseRecommenderModel

Adversarial Multimedia Recommender

For further details, please refer to the paper

Parameters

factors – Number of latent factor
factors_d – Image-feature dimensionality
lr – Learning rate
l_w – Regularization coefficient
l_b – Regularization coefficient of bias
l_e – Regularization coefficient of image matrix embedding
eps – Perturbation Budget
l_adv – Adversarial regularization coefficient
adversarial_epochs – Adversarial epochs

To include the recommendation model, add it to the config file adopting the following pattern:

models:
  AMR:
    meta:
      save_recs: True
    epochs: 10
    factors: 200
    factors_d: 20
    lr: 0.001
    l_w: 0.1
    l_b: 0.001
    l_e: 0.1
    eps: 0.1
    l_adv: 0.001
    adversarial_epochs: 5